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Efficient Technique for Annonymized Microdata Preservation using Slicing
| Content Provider | CiteSeerX |
|---|---|
| Author | Mirashe, Manjusha S. Hande, N. |
| Abstract | Abstract: Data Anonymization is always being a subject of researchers in the last few years. Privacy Preserving Data Mining, i.e. the study of data mining side-effects on privacy, which receives an increasing attention from the research community. Privacy-preservation data publishing has received lot of attention, as it is always a problem of how to secure a database of high dimension. In much organization where large number of confidential data is available, such data must be secured. The personal data may be misused, for a variety of purposes. In order to alleviate these concerns, a number of techniques have recently been proposed in order to perform the data mining tasks in a privacy-preserving way. There are several anonymization techniques available such as generalization and bucketization that are designed for privacy preservation of microdata publishing. But it has been seen that for high dimension data generalization looses the information, bucketization on other hand does not prevent membership disclosure. We present another anonymization technique known as Slicing. The significance of using slicing is that it can handle high dimension data. Slicing preserves better data utility than generalization and also prevents membership disclosure. This paper focus on effective method that can be used for providing better data utility and can handle high-dimensional data. Keywords- Data anonymization, Data publishing, Data security, Privacy Preservation, Privacy Threats |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Efficient Technique Annonymized Microdata Preservation Privacy Preservation Data Utility Privacy-preserving Way Data Publishing Data Mining Task Personal Data Data Mining High-dimensional Data Data Anonymization Several Anonymization Technique Privacy Threat Confidential Data High Dimension Data Generalization Paper Focus Keywords Data Anonymization High Dimension Data Prevents Membership Disclosure Membership Disclosure Microdata Publishing Privacy-preservation Data Publishing Effective Method Much Organization Anonymization Technique Research Community High Dimension Data Security |
| Content Type | Text |
| Resource Type | Article |
